Abstract
Path prediction of autonomous vehicles is an essential requirement under any given traffic scenario. Trajectory of several agent vehicles in the vicinity of ego vehicle, at least for a short future, is needed to be predicted in order to decide upon the maneuver of the ego vehicle. We explore variational autoencoder networks to obtain multimodal trajectories of agent vehicles. In our work, we condition the network on past trajectories of agents and traffic scenes as well. The latent space representation of traffic scenes is achieved by using another variational autoencoder network. The performance of the proposed networks is compared against a residual baseline model.
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References
Brännström, M., Coelingh, E., Sjöberg, J.: Model-based threat assessment for avoiding arbitrary vehicle collisions. IEEE Trans. Intell. Transp. Syst. 11(3), 658–669 (2010)
Ortiz, M.G., Fritsch, J., Kummert, F., Gepperth, A.: Behavior prediction at multiple time-scales in inner-city scenarios. In: IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany, pp. 1068–1073 (2011)
Lawitzky, A., Althoff, D., Passenberg, C.F., Tanzmeister, G., Wollherr, D., Buss, M.: Interactive scene prediction for automotive applications. In: IEEE Intelligent Vehicles Symposium (IV), Gold Coast, QLD, Australia, pp. 1028–1033 (2013)
Oliver, N., Pentland, A.P.: Graphical models for driver behavior recognition in a smartcar. In: IEEE Intelligent Vehicles Symposium (Cat. No. 00TH8511), Dearborn, MI, USA, pp. 7–12 (2000)
Agamennoni, G., Nieto, J.I., Nebot, E.M.: Estimation of multivehicle dynamics by considering contextual information. IEEE Trans. Rob. 28(4), 855–870 (2012)
Gindele, T., Brechtel, S., Dillmann, R.: A probabilistic model for estimating driver behaviors and vehicle trajectories in traffic environments. In: 13th International IEEE Conference on Intelligent Transportation Systems, Funchal, Portugal, pp. 1625–1631 (2010)
Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., Alahi, A.: Social GAN: socially acceptable trajectories with generative adversarial networks. In: IEEE Conference on Computer Vision and Pattern (2018)
Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S.: Social LSTM: human trajectory prediction in crowded spaces. In: IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp. 961–971 (2016)
Zhan, W., La de Fortelle, A., Chen, Y.T., Chan, C.Y., Tomizuka, M.: Probabilistic prediction from planning perspective: problem formulation, representation simplification and evaluation metric. In: IEEE Intelligent Vehicles Symposium (IV), Changshu, China, pp. 1150–1156 (2018)
Mozaffari, S., Al-Jarrah, O.Y., Dianati, M., Jennings, P., Mouzakitis, A.: Deep learning-based vehicle behavior prediction for autonomous driving applications: a review. IEEE IEEE Trans. Intell. Transp. Syst. (2020). https://doi.org/10.1109/TITS.2020.3012034
Houston, J., et al.: One thousand and one hours: self-driving motion prediction dataset. arXiv preprint, arXiv:2006.14480 (2020)
Zyner, A., Worrall, S., Nebot, E.: A recurrent neural network solution for predicting driver intention at unsignalized intersections. IEEE Robot. Autom. Lett. 3(3), 1759–1764 (2018)
Zyner, A., Worrall, S., Ward, J., Nebot, E.: Long short-term memory for driver intent prediction. In: IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA, pp. 1484–1489 (2017)
Xin, L., Wang, P., Chan, C.Y., Chen, J., Li, S.E., Cheng, B.: Intention-aware long horizon trajectory pre-diction of surrounding vehicles using dual LSTM networks. In: 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, pp. 1441–1446 (2018)
Hammer, B.: On the approximation capability of recurrent neural networks. Neurocomputing 31(1–4), 107–123 (2000)
Deo, N., Trivedi, M.M.: Multi-modal trajectory prediction of surrounding vehicles with maneuver based LSTMs. In: IEEE Intelligent Vehicles Symposium (IV), Changshu, China, pp. 1179–1184 (2018)
Phillips, D.J., Wheeler, T.A., Kochenderfer, M.J.: Generalizable intention prediction of human drivers at intersections. In: IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA, pp. 1665–1670 (2017)
Dai, S., Li, L., Li, Z.: Modeling vehicle interactions via modified LSTM models for trajectory prediction. IEEE Access 7, 38287–38296 (2019)
Hu, Y., Zhan, W., Tomizuka, M.: Probabilistic prediction of vehicle semantic intention and motion. In: IEEE Intelligent Vehicles Symposium (IV), Changshu, China, pp. 307–313 (2018)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint, arXiv:1406.1078 (2014)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Cui, H., et al.: Multi-modal trajectory predictions for autonomous driving using deep convolutional networks. In: International Conference on Robotics and Automation (ICRA), Montreal, Canada, pp. 2090–2096 (2019)
Djuric, N., et al.: Motion prediction of traffic actors for autonomous driving using deep convolutional networks. arXiv preprint, arXiv:1808.05819, 2 (2018)
Lee, N., Choi, W., Vernaza, P., Choy, C.B., Torr, P.H., Chandraker, M.: Desire: distant future prediction in dynamic scenes with interacting agents. In: IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp. 336–345 (2017)
Luo, W., Yang, B., Urtasun, R.: Fast and furious: real time end-to-end 3D detection, tracking and motion fore-casting with a single convolutional net. In: IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, US, pp. 3569–3577 (2018)
Zhao, T., et al.: Multi-agent tensor fusion for contextual trajectory prediction. In: IEEE Conference on Computer Vision and Pattern Recognition, California, US, pp. 12126–12134 (2019)
Jagadish, D.N., Chauhan, A., Mahto, L: Deep learning techniques for autonomous vehicle path prediction. In: AAAI Workshop on AI for Urban Mobility. Vancouver, Canada (2021)
Hoermann, S., Bach, M., Dietmayer, K.: Dynamic occupancy grid prediction for urban autonomous driving: A deep learning approach with fully automatic labeling. In: IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, pp. 2056–2063 (2018)
Schreiber, M., Hoermann, S., Dietmayer, K.: Long-term occupancy grid prediction using recurrent neural networks. In: International Conference on Robotics and Automation (ICRA), Montreal, Canada, pp. 9299–9305 (2019)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition, Las Vegas, US, pp. 770–778 (2016)
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Jagadish, D.N., Chauhan, A., Mahto, L. (2021). Autonomous Vehicle Path Prediction Using Conditional Variational Autoencoder Networks. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12712. Springer, Cham. https://doi.org/10.1007/978-3-030-75762-5_11
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DOI: https://doi.org/10.1007/978-3-030-75762-5_11
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